Sentence Embedding Generation Framework Based on Kullback–Leibler Divergence Optimization and RoBERTa Knowledge Distillation

Sentence Embedding Generation Framework Based on Kullback–Leibler Divergence Optimization and RoBERTa Knowledge Distillation

In natural language processing (NLP) tasks, computing semantic textual similarity (STS) is crucial for capturing nuanced semantic differences in text. Traditional word vector methods, such as Word2Vec and GloVe, as well as deep learning models like BERT, face limitations in handling context dependen...

Full description

Saved in:
Bibliographic Details
Main Authors: Jin Han, Liang Yang
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Mathematics
Subjects:
semantic textual similarity (STS)
Kullback–Leibler divergence (KLD)
knowledge distillation
feature selection
similarity evaluation
sentence embedding
Online Access:https://www.mdpi.com/2227-7390/12/24/3990
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://www.mdpi.com/2227-7390/12/24/3990

Similar Items